Method and apparatus for categorizing and presenting documents of a distributed database

ABSTRACT

Described herein are methods for creating categorized documents, categorizing documents in a distributed database and categorizing Resulting Pages. Also described herein is an apparatus for searching a distributed database. The method for creating categorized documents generally comprises: initially assuming all documents are of type 1; filtering out all type 2 documents and placing them in a first category; filtering out all type 3 documents and placing them in a second category; and defining all remaining documents as type 4 documents and placing all type 4 documents in a third category. The apparatus for searching a distributed database generally comprises at least one memory device; a computing apparatus; an indexer; a transactional score generator; and a category assignor; a search server; and a user interface in communication with the search server.

BACKGROUND

The transfer of information over computer networks has become anincreasingly important means by which institutions, corporations, andindividuals do business. Computer networks have grown over the yearsfrom independent and isolated entities established to serve the needs ofa single group into vast internets which interconnect disparate physicalnetworks and allow them to function as a coordinated system. Currently,the largest computer network in existence is the Internet. The Internetis a worldwide interconnection of computer networks that communicateusing a common protocol. Millions of computers, from low end personalcomputers to high end supercomputers, are connected to the Internet.

The Internet has emerged as a large community of electronicallyconnected users located around the world who readily and regularlyexchange vast amounts of information. The Internet continues to serveits original purposes of providing for access to and exchange ofinformation among government agencies, laboratories, and universitiesfor research and education. In addition, the Internet has evolved toserve a variety of interests and forums that extend beyond its originalgoals. In particular, the Internet is rapidly transforming into a globalelectronic marketplace of goods and services as well as of ideas andinformation.

This transformation of the Internet into a global marketplace was drivenin large part by the introduction of common protocols such as HTTP(HyperText Transfer Protocol) and TCP/IP (Transmission ControlProtocol/Internet Protocol) for facilitating the easy publishing andexchange of information. The Internet is thus a unique distributeddatabase designed to give wide access to a large universe of documentspublished from an unlimited number of users and sources. The databaserecords of the Internet are in the form of documents known as “pages” orcollections of pages known as “sites.” Pages and sites reside on serversand are accessible via the common protocols. The Internet is therefore avast database of information dispersed across seemingly countlessindividual computer systems that is constantly changing and has nocentralized organization.

Computers connected to the Internet may access pages via a program knownas a browser, which has a powerful, simple-to-learn user interface,typically graphical and enables every computer connected to the Internetto be both a publisher and consumer of information. Another powerfultechnique enabled by browsers are known as hyperlinking, which permitspage authors to create links to other pages that users can then retrieveby using simple commands, for example pointing and clicking within thebrowser. Thus each page exists within a nexus of semantically relatedpages because each page can be both a target and a source forhyperlinking, and this connectivity can be captured to some extent bymapping and comparing how those hyperlinks interrelate. In addition, thepages may be constructed in any one of a variety of syntaxes, such asHyper Text Markup Language (HTML) or eXstensible Markup Language (XML),and may include multimedia information content such as graphics, audio,and still and moving pictures.

Because any person with a computer and a connection to the Internet maypublish their own page on the Internet as well as access any otherpublicly available page, the Internet enables a many-to-many model ofinformation production and consumption that is not possible or practicalin the offline world. Effective search services, including searchengines, are an important part of the many-to-many model, enablinginformation consumers to rapidly and reliably identify relevant pagesamong a mass of irrelevant yet similar pages. Because of themany-to-many model, a presence on the Internet has the capability tointroduce a worldwide base of consumers to businesses, individuals, andinstitutions seeking to advertise their products and services toconsumers who are potential customers. Furthermore, the ever increasingsophistication in the design of pages, made possible by the exponentialincrease in data transmission rates, computer processing speeds andbrowser functionality makes the Internet an increasingly attractivemedium for facilitating and conducting commercial transactions as wellas advertising and enabling such transactions. Because the Internetallows direct identification of and connection between businesses andtargeted consumers, it has the potential to be a powerfully effectiveadvertising medium.

The availability of powerful new tools that facilitate the developmentand distribution of Internet content (this includes information of anykind, in any form or format) has led to a proliferation of information,products, and services offered through the Internet and a dramaticgrowth in the number and types of consumers using the Internet.International Data Corporation, commonly referred to as IDC, hasestimated that the number of Internet users will grow to approximately320 million worldwide by the end of 2002. In addition, commerceconducted over the Internet has grown and is expected to growdramatically. IDC estimates that the percentage of Internet users buyinggoods and services on the Internet will increase to approximately 40% in2002, and that the total value of goods and services purchased over theInternet will increase to approximately $425.7 billion.

Thus, the Internet has emerged as an attractive new medium foradvertisers of information, products and services (“advertisers”) toreach not only consumers in general, but also to enable increasedcapabilities to identify and target specific groups of consumers basedon their preferences, characteristics or behaviors. However, theInternet is composed of an unlimited number of sites dispersed acrossmillions of different computer systems all over the world, and soadvertisers face the daunting task of locating and targeting thespecific groups or subgroups of consumers who are potentially interestedin their information, products and/or services.

Advertisers, rely on search services to help consumers locate theadvertisers' sites. Search services, including directories and searchengines, have been developed to index and search the informationavailable on the Internet and thereby help users, including consumers,locate information, products and services of interest. These searchservices enable users, including consumers, to search the Internet for alisting of sites based on a specific keyword topic, product, or serviceof interest as described by the users in their own language. Becausesearch services are the most frequently used tool on the Internet afteremail, sites providing search services offer advertisers significantreach into the Internet audience and create the opportunity to targetconsumer interests based on keyword or topical search requests.

Search services are generally created by search engine providers whoelectronically review the pages of the Internet and create an index anddatabase based on that review. The search engine providers may offer thesearch services directly to consumers or may provide the search servicesto a third party who then provides the search services to consumers.Usually, the databases are created either by crawling the Internet andmaking a local copy of every page or aspect thereof into a memorydevice, or by collecting submissions from the providers of the pages(the “Resulting Pages”). This can include static and/or dynamic content,whether text, image, audio, video or still images. Alternatively, onlycertain aspects of the pages may be copied such as the URL, title ortext. Each Resulting Page is indexed for later reference. Thus when asearch of the Internet is requested by a user, the search engine doesnot actually search the Internet in real-time, but rather searches itsown index and database for the relevant Resulting Pages (“searchresults” or “listings”). The search results are then presented to theuser as either copies of the actual pages or a listing of pages that maybe accessed via hyperlink.

Many known search engines use automated search technology to catalogsearch results which generally rely on invisible site descriptions knownas “meta tags” that are authored by site promoters. Because advertisersmay freely tag or have tagged their sites as they choose, many pages aregiven similar meta-tags, which increase the difficulty of providingrelevant search results. In addition, most known search engines rely ontheir own hierarchy of semantic categories into which indexed pages arecategorized. This is a top-down categorization approach where thecategories are semantically related irrespective of their commercial ornon-commercial nature. Therefore, known search engines do not provide abottom-up, customizable categorization of search result based upon thepage or site's commercial nature and relevance.

Additionally, some advertisers and other site promoters insert popularsearch terms into their site's meta tags which are not relevant to theirpages so that these pages may attract additional consumer attention atlittle to no marginal cost. Such pages yield many undesirable resultsand are referred to as “spam pages.” Generally, pages are referred to as“spam” if they include some mechanism for the purpose of deceivingsearch engines and/or relevance ordering algorithms and may alsoredirect users towards sites that are not relevant to the user'soriginal search. Many such mechanisms and techniques exist and include,but are not limited to including meta tags that do not reflect the truenature of the page. Usually, spam pages are commercial in nature. Thatis, they attempt to sell something to users.

Many known search engines are simply not equipped to prioritize resultsin accordance with consumers' preferences. Known search engines also donot provide any way to determine whether each page in a listing iscommercial in nature and to categorize the listing on the basis of thecommercial nature of each page. When this is done, the search resultscan be processed to provide a more useful organization according to theconsumer's intent (whether it be to carry out a commercial transactionor to seek information) in initiating the search. For example, aconsumer seeking information on a given topic may wish to distinguishpages that are primarily informational in nature from pages that areprimarily commercial in nature. In another example, a consumer may wishto distinguish pages that are primarily commercial in nature andrelevant to the consumer's request, from unwanted or spam pages.

Moreover, in known search engines, a consumer attempting to locate asite for purchasing goods or services will also be presented with a vastnumber of sites that might relate to the item but do not facilitate thepurchase of that item. Likewise, consumers interested only in locatinginformational sites for an item will also be presented with manycommercial sites for purchasing the item that may not provide theinformation they are seeking. Therefore, the consumer's desired resultpages are hidden among large numbers of pages that do not correspondwith the consumer's ultimate goal because known search engines are notable to distinguish either the consumer's intent for the search nor thecommercial or non-commercial nature of the search results.

Thus, the known search engines do not provide an effective means forusers to categorize the type of search results for which they arelooking, informational or commercial, or for advertisers seeking tocontrol their exposure and target their distribution of information tointerested consumers. Current paradigms for presenting search resultsmake no page by page distinction between informational and commercialsources of information, and instead mix both types of results dependingpurely on the relevance assigned to them as responses to the user'soriginal search query.

Known methods used by advertisers to control their exposure and targettheir distribution, such as banner advertising, follow traditionaladvertising paradigms and fail to utilize the unique attributes of theInternet's many-to-many publishing model. Furthermore, to the extentthat banner ads are found in the search results, they often fail toattract consumer interest because the consumer is looking in a directedmanner for search results on that page, not for a banner.

Thus, the traditional paradigms relating to Internet advertising andsearch engines fail to effectively categorize and deliver relevantinformation to interested parties in a timely and cost-effective manner.Therefore, consumers must manually sort through all search results toultimately locate the type of results (commercial or non-commercial) inwhich they are interested. Because Internet advertising can, however,offer a level of targetability, interactivity, and measurability notgenerally available in other media, the ability to categorize andclearly present identified sets of commercial and non-commercial resultsincreases consumer satisfaction and facilitates increased economicefficiency by reducing the amount of manual sorting required of users.

Ideally, advertisers should be able to improve their visibility in anInternet search results list so that their pages not only appearprominently in the listing but are not masked by a multitude of othernon-commercial pages. (see U.S. Pat. No. 6,269,361, incorporated hereinby reference). Likewise consumers should be able to have their searchresults reliably categorized and clearly presented as eitherinformational or commercial. Without a reliable means to distinguishbetween commercial and non-commercial pages, known search engines cannotexploit the true potential of the targeted market approach made possibleby the Internet.

Thus, the search engine functionality of the Internet needs to befocused in a new direction to facilitate an online marketplace whichoffers consumers quick, relevant and customizable search results whilesimultaneously providing advertisers with a reliable, verifiable andcost-effective way to target consumers and position the advertisers'products and services within a listing. A consumer utilizing a searchengine that facilitates this on-line marketplace will find companies orbusinesses that offer the products or services that the consumer isseeking without the distraction of non-commercial pages. Additionally,while the user is seeking strictly informational resources, the userwill not be bothered by spam pages or irrelevant commercial pages.

It is therefore an object of the present invention to provide a systemand method for examining and categorizing records in a distributeddatabase as commercial or non-commercial records and then presentingthose records in response to a database query submitted by a user ornetwork-defined settings.

It is also an object of this invention is to provide users with acustomizable search engine that permits users to organize search resultslistings based upon the commercial nature of the search result and toallow users to specify presentation rules based upon categories and userpreferences.

A further object of this invention is to provide search engine servicecustomers with a customizable search engine that permits each searchengine service customer to organize search results listings based uponthe commercial nature of the search result and to allow the searchengine service customer to specify presentation rules for the searchresults based upon categories and search engine service customerpreferences.

It is also an object of the present invention is to provide a system andmethod for enabling search engine service providers or users todynamically specify the importance of various transactional criteria andthreshold values in order to create a flexible scale of value based onthe commercial nature of a record in order to assign a transactionalrating and therefore a commercial or non-commercial designation for eachrecord.

A further object of the present invention is to provide a system andmethod for categorizing and presenting search results by combining atransactional rating with a quality score and a spam score in order toassign a commercial score and then rank or classify such resultsaccording to such score.

It is also an object of the present invention to provide a system andmethod for categorizing documents in a distributed database to createcategorized documents by initially assuming all documents arenon-commercial, filtering out all commercial documents and placing themin a first category and using the first category as a collection ofadvertiser prospects for a pay for performance search engine.

A further object of the present invention is to provide a cost-effectivesystem and method for managing the operation of a pay for performancesearch engine by automatically generating advertiser sales leads byinitially categorizing pages as commercial or non-commercial and thenfurther categorizing commercial pages as existing customers or salesleads.

A further object of the present invention is to provide a system andmethod for categorizing records in a distributed database to identifycommercial records and compare those records against a pay forperformance search engine's listings in order to further categorizecommercial records as either participating advertisers ornon-participating advertisers.

A still further object of the present invention is to provide a systemand method of sales lead generation for pay for performance searchengine advertisers by organizing and presenting non-participatingcommercial records to a pay for performance search engine sales staffaccording to dynamically specified criteria.

BRIEF DESCRIPTION

Described herein are methods for creating categorized documents,categorizing documents in a distributed database and categorizingResulting Pages. Also described herein is an apparatus for searching adistributed database.

The method for creating categorized documents generally comprises:initially assuming all documents are of type 1; filtering out all type 2documents and placing them in a first category; filtering out all type 3documents and placing them in a second category; and defining allremaining documents as type 4 documents and placing all type 4 documentsin a third category.

The method for categorizing documents in a distributed databasegenerally comprises: assuming all documents in the distributed databaseare non-commercial in nature; filtering out all documents that arecommercial in nature from the documents, wherein the documents that arecommercial in nature are commercial documents; and creating sales leadsfrom the commercial documents. In one embodiment of this method, thedocuments are pages and the distributed database is the Internet.

A method for categorizing Resulting Pages into categories generallycomprises: designating a first category as commercial pages and a secondcategory as informational pages; determining a quality score q(wi) foreach Resulting Page; determining a transactional rating for eachResulting Page τ(w_(i)); deriving a propagation matrix; P determining acommercial score κ for each Resulting Page; filtering out all ResultingPages that meet or exceed a commercial score threshold value; whereinthe Resulting Pages that meet or exceed the commercial page thresholdvalue are placed in the first category and all remaining Resulting Pagesare placed in the second category.

A further method for categorizing a plurality of Resulting Pages intocategories generally comprises: determining whether each of theplurality of Resulting Page is a spam page; determining a quality scoreq(wi) for each of the plurality of Resulting Pages; determining atransactional rating τ(w_(i)) for each of the plurality of ResultingPages; deriving a propagation matrix P; determining a commercial score κfor each of the plurality of Resulting Pages; filtering out allspam-inclusive commercial pages from the plurality of Resulting Pages;filtering out all spam pages from the spam-inclusive commercial pages;placing all commercial pages in a commercial category; and placing allremaining Resulting Pages into an information category.

A method for searching a distributed database generally comprises: (a)entering search terms or phrases into a system; (b) generating documentscontaining keywords that match the search terms or phrases; (c)categorizing search results into categories according to categorizationcriteria to create categorized documents; and (d) presenting thecategorized documents.

Also described herein is a search engine and database for a distributeddatabase, generally comprising at least one memory device, comprising,at least one Internet cache and an Internet index; a computingapparatus, comprising, a crawler in communication with the Internetcache and the Internet; an indexer in communication with the Internetindex and the Internet cache; a transactional score generator incommunication with the Internet cache; and a category assignor incommunication with the Internet cache; a search server in communicationwith the Internet cache, the Internet index; and a user interface incommunication with the search server.

The system provides numerous embodiments that will be understood bythose skilled in the art based on the present disclosure. Some of theseare described below and are represented in the drawings by means ofseveral figures, in which:

BRIEF DESCRIPTION OF SEVERAL VIEWS OF THE DRAWINGS

FIG. 1A is a block diagram of page categorization, according to anembodiment of the present invention;

FIG. 1B is a is a block diagram of page categorization, according toanother embodiment of the present invention;

FIG. 2 is a flow chart of a system for determining whether a page is aCommercial Page, according to an embodiment of the present invention;

FIG. 3 is a flow chart of a system for determining a transaction ratingfor a page, according to an embodiment of the present invention;

FIG. 4 is a flow chart of a system for creating a propagation matrix,according to an embodiment of the present invention;

FIG. 5 is a flow chart of a system for providing customizedcategorization of search results, according to an embodiment of thepresent invention;

FIG. 6 is a flow chart of a system for providing customized searchresults and the presentation of the customized search results, accordingto an embodiment of the present invention;

FIG. 7 is a flow chart of a system for automating the collection ofsales leads for a pay for performance search engine sales staff,according to an embodiment of the present invention; and

FIG. 8 is a diagram of an apparatus for categorizing and displayingsearch results, according to an embodiment of the present invention.

DETAILED DESCRIPTION

Described herein is a method and apparatus for identifying documents ina distributed database. One embodiment comprises a heuristic foridentifying pages that are commercial in nature and providing a systemand method for the dynamic categorization and presentation of bothcommercial pages and informational pages in real-time to an advertiser,search engine provider or user. This system may be used in any contextwhere it is useful to categorize search results based upon thecommercial nature of those pages, and can be utilized in a multitude offorms from a browser plug-in to a stand-alone application to a back-endsearch-engine or search engine tool. In addition, the system can be usedto provide unique operational benefits to a pay for performance searchengine provider by automating a portion of the sales cycle and enablinga collaborative account management environment between advertisers and athe pay for performance search engine provider.

Distinct sets of search results for commercial pages and informationalpages returned in response to a user-defined query, are provided toadvertisers, search engine service providers and users. The systemdistinguishes pages according to the commercial nature of each page, andthereby provides more relevant results by providing relevant searchresults to those users seeking information or to enter into a commercialtransaction, without confusing the two categories of search results. Thesystem also enables complete customization with regard to the set ofcriteria used to categorize search results, the importance of each suchcriterium in the determination of such categorization, and the ultimatecategorization and presentation of such search results to the user.

Methods and apparatuses for statically and dynamically categorizing andpresenting the records of a distributed database are disclosed.Descriptions of specific embodiments are provided only as examples, andvarious modifications will be readily apparent to those skilled in theart and are not intended to be limited to the embodiments described.Identical features are marked with identical reference symbols in theindicated drawings.

Described herein is a customizable system for identifying andcategorizing the records in or the results of a search of the records ina distributed database, and for categorizing and presenting the recordsor search results according to the commercial nature of the record in amore organized, more easily understood, and therefore, more usefulmanner. The following descriptions detail how the pages of or theresults of a search of the Internet may be identified and categorized ascommercial and non-commercial (informational), but it is readilyunderstood that the records of a distributed database, including theInternet, may be categorized into a limitless variety of categories,including sub-categories of the commercial and non-commercialcategories. Other categories may include on-line shopping andadvertisements for traditional stores and services. Alternatively, oradditionally, the records in or the search results of the records in adistributed database may be categorized and presented geographically,via price range, and by many other criteria according to a variety ofuser-specified variables. Additionally, the methods disclosed herein maybe used across any distributed database coupled in any manner to anykind of network including Local Area Networks (LAN) and Wide AreaNetworks (WAN), and not just the Internet.

Referring now to the drawings, FIGS. 1A and 1B show how the searchresults of a search of the Internet can be categorized. A search of theInternet is actually a search of a database of the contents of theInternet that can be generated through the use of a crawler. The crawlercrawls the Internet and saves to a local database either a duplicate ofeach page found or a duplicate of a portion thereof (the portion mayinclude any of the following features of each Internet page found: theURL, titles, content, brief description of the content, hyperlinks orany combination thereof). The local copies of the pages or portionsthereof may then be searched using a search engine. The local copies ofthe pages, portions thereof or any pages or portions thereof that arethe result of a search of the foregoing are all considered “ResultingPages”.

As shown in FIGS. 1A and 1B, the Resulting Pages 50 can generally becategorized as commercial, and non-commercial. Resulting Pages in thecommercial category (“Commercial Pages”) 52, 62 generally include thoseResulting Pages that facilitate the buying and/or selling of goodsand/or services or that evince an intent to conduct commercial activityby the publisher of that page (are commercial in nature). For example,Commercial Pages 52, 62 include pages that offer goods and/or servicesvia sale, lease, trade, or other such transaction, or that providecontact information for such transactions to be made by some other meanssuch as facsimile, telephone or in-person. Resulting Pages in thenon-commercial category (“Non-Commercial Pages”) 54, 64 generallyinclude those that are informational in nature and do not facilitate thebuying and/or selling of goods and/or services and hence are notcommercial in nature. Non-Commercial Pages may alternately be called“Informational Pages.”

Resulting Pages that are spam (“Spam Pages”) are generally considered tobe a subset of the Commercial Pages 52, 62, because Spam Pages 56 aregenerally commercial in nature. However, it is also possible for SpamPages to be primarily informational in nature because Spam Pages provideinformation regarding goods and/or services, but do not themselvesfacilitate the buy of goods and/or services. Because, Spam Pages aredesigned to deceive or degrade search engines, includingrelevance-ordering heuristics, they are generally undesirable and may beremoved or excluded from the search results. Usually, Spam Pages areconsidered commercial in nature because they provide a direct link toother pages that are commercial in nature\. Spam pages can becategorized as Commercial Pages, as shown in FIGS. 1A and 1B, or,alternatively, excluded from the commercial category.

In one embodiment of the invention, Resulting Pages may be furthercategorized in the premium-content containing category (“PCC Pages”).PCC Pages are pages for which payment of a premium is required in orderto gain access to the content. In some cases, payment of the premium isgoverned by an agreement or contract. There are many examples of PCCPages such as those found at the following URLs: www.law.com andwww.northernlight.com. PCC Pages can be considered either a subset ofCommercial Pages and be placed in the Commercial category or a subset ofNon-Commercial Pages and be placed in the Non-Commercial Categorydepending on the preferences of the user or search engine servicecustomer. For example, PCC Pages 58 require payment of a premium inorder to gain access. Because of the payment requirement, they have acommercial nature and may be considered a subset of the CommercialPages, as shown in FIG. 1A. On the other hand, PCC Pages generallyprovide information and do not facilitate the buying and/or selling ofgoods and/or services other than the information contained on the PCCPages themselves. Therefore, they also have an informational nature andmay be considered a subset of the Non-Commercial Pages, as shown in FIG.1B.

Yet another embodiment for filtering out the Commercial Pages andplacing them in the commercial category generally comprises the stepsshown in FIG. 2, indicated by reference numeral 10. These steps include:determining whether each page is a Spam Page 12; determining a qualityscore for each page 14; determining a transactional rating for each page16; deriving a propagation matrix 18; determining a commercial score foreach page 20; filtering out all pages with a commercial score that meetsor exceeds a threshold value (the “Spam-Inclusive Commercial Pages”) 22;filtering out the Spam Pages from the Spam-Inclusive Commercial Pages24; and placing the Commercial Pages into the Commercial category 26.

In one embodiment, determining whether a page is a Spam Page involvescomputing a spam score, σ(w_(i)) for each page and determining whetherthe spam score meets or exceeds the threshold value assigned to the spamscore. The pages that meet or exceed the spam score threshold value areSpam Pages. Determining the spam score can be accomplished using knowntechniques, such as, having a human assign a score, and the automatedtechniques presented in the following papers, which are herebyincorporated by reference: a white paper by ebrandmanagement.comentitled “The Classification of Search Engine Spam” and a paper by DannySullivan entitled “Search Engine Spamming.” Both documents appear in theProceedings of Search Engine Strategies, Mar. 4–5 2002, Boston, Mass.,organized by Danny Sullivan. The foregoing and other known methodsinclude both manual and automatic evaluation methods. These methods andsimilar machine-learning techniques could also be applied to computingtau (τ), the initial vector in equation (12) described later herein.

The quality score, q(w_(i)), is a scalar value that is a measure of thequality of a page. In one embodiment, determining the quality score ofthe pages includes evaluating a subset of pages against a select groupof criteria. Criteria against which the quality of the page may bejudged include quality of the content, reputation of the author orsource of information, the ease of use of page and many other suchcriteria. The quality score may be human-assigned or determinedautomatically, and a default value may be assigned to pages notexplicitly evaluated.

A transactional rating is a scalar value that represents whether or howstrongly a page facilitates transactions, such as a sale, lease, rentalor auction. In one embodiment, the steps for determining a transactionalrating for each page are shown generally in FIG. 3 and indicated byreference number 16. Transactional ratings are determined from atransactional score. A transactional score is a vector that representswhether or how strongly each page meets a specified set of criteria.

Therefore, the first step is to determine whether a page and/or thepage's URL meet select criteria 32. There are many, many characteristicsof a page that can be examined in order to ultimately determine whetherthe page is transactional in nature. These criteria include, determiningwhether the page includes the following: a field for entering creditcard information; a field for a username and/or password for an onlinepayment system such as PayPal™ or BidPay™, a telephone number identifiedfor a “sales office,” a “sales representative,” “for more informationcall,” or any other transaction-oriented phrase; a link or button withtext such as “click here to purchase,” “One-Click™ purchase,” or similarphrase, text such as “your shopping cart contains” or “has been added toyour cart,” and/or a tag such as a one-pixel GIF used for conversiontracking. Any text matching may be either on text strings, such assequences of characters in the Unicode or ASCII character sets, or ontext derived from optical character recognition of text rendered inimages, or speech recognition on a sound recording presented in responseto an http (Hyper Text Transfer Protocol) request. The criteria can beused in any combination and any individual criteria may be used or notused. Additionally, these criteria are only examples and do notconstitute an exhaustive list.

For each page, it must then be determined how strongly the page meetsthe selected criteria, block 34. Various techniques exist fordetermining whether pages meet certain criteria, 32, and how stronglythey meet these criteria 34. For instance, each page may be examined bya human editor and evaluated in terms of the criteria and assignedeither a Boolean value or a weighted value. This, however, is a veryslow and subjective process. Much faster automated techniques include,automatically checking for or counting string matches, image matches ormatches of string length and/or matches of data entry field type (suchas numeric or alphanumeric) and assigning a log-likelihood score usinglanguage models. Language models include, for example, n-gram wordtransition models as described in Statistical Methods for SpeechRecognition, Jenek, 1999. These methods can assign a Boolean number or aweighted value.

Using the results obtained by determining whether each page and/or itsURL meet select criteria, 32, and determining how strongly the pageand/or its URL meet select criteria, 34, a transactional score isdetermined, 35. Determining the transactional score 35 for each pageincludes creating a vector αk(w_(i)) or a vector βk(w_(i)) from theresults of blocks 32 and 34, respectively. One of these vectors iscreated for each page “w_(i)”, wherein the index “i” represents aparticular page and the index “k” represents a particular criterionagainst which the page was evaluated. The number of elements in thevector “n” (1≦j≦n) is determined by the number of criteria used and thenumber of vectors is determined by the number of pages “m.” Thetransactional score α_(n)(w_(i)) is a vector of Boolean values wherein a“0” for a given criteria indicates that that criteria is not met (false)and any chosen integer “p” for a given criteria indicates that thatcriteria is met (true). The transactional score vector β_(n)(w_(i)) hasthe same number of elements as α(w_(i)). However, the elements inβ_(n)(w_(i)) can include any range of real numbers wherein each numberindicates how strongly a page meets the criteria. For instance,β_(n)(w_(i)) may include the real numbers between “0” and “1” (althoughit can include any range of real numbers) wherein “0” represents that acriterion is not met at all and “1” represents that a criteria iscompletely met. The real numbers between “0” and “1” represent thevarious degrees to which a criterium is met.

Transactional scores αk_(n)(w_(i)) and βk_(n)(w_(i)) are used todetermine alternate values for the transactional rating τ(w_(i)) foreach page, wherein:

$\begin{matrix}{{\tau\left( w_{i} \right)} = {{{\alpha\left( w_{i} \right)}}_{\rho} = \left( {\sum\limits_{i = 1}^{n}\;{{\alpha\left( w_{i} \right)}}^{\rho}} \right)^{- \rho}}} & (1)\end{matrix}$

-   -   alternately:

$\begin{matrix}{{\tau\left( w_{i} \right)} = {{{\beta\left( w_{i} \right)}}_{\rho} = \left( {\sum\limits_{i = 1}^{n}\;{{\beta\left( w_{i} \right)}}^{\rho}} \right)^{- \rho}}} & (2)\end{matrix}$

The transactional rating τ(w_(i)) is a scalar value that is the ρ-normof either the vector α_(n)(w_(i)) or the vector β_(n)(w_(i)). “n” is thenumber of criteria used in evaluating each site w_(i). Generally, ρ=2 sothat no single weighted criterion dominates the others. However, ρ canbe altered to give more weight to the most dominant criteria, ifdesired. Either formula (1) or (2) may alternately be used to determinethe transactional rating. Formula (2) reflects the degree to whichindividual criteria are met.

The steps for deriving the propagation matrix are shown generally inFIG. 4 as reference numeral 18. The steps comprise, creating a hyperlinkconnectivity matrix 42, calculating transition counts and page views,44, and creating a propagation matrix 46. A hyperlink connectivitymatrix is a way of representing the link structure of the Internet,World Wide Web or any set of hyperdocuments and the relative importanceor relevance of each page. In this embodiment, the relative importanceof each page is determined by examining the number of links from eachpage w_(i), to each page w_(j), and from each page, w_(j), to each page,w_(i). These links are represented in the hyperlink connectivity matrix.The hyperlink connectivity matrix “C” has “m” rows and “m” columns. Thenumber of rows and columns “m” equals the number of pages, wherein aspecific row is indicated by index “i” and a specific column isindicated by column “j.” Each element in this matrix, C_(ij), willcontain a value of “1” if and only if a page w_(i) links to another pagew_(j), otherwise it will contain a “0”.

The hyperlink connectivity matrix is then used to calculate two scalarvalues, the authority score a_(i) and the hub score h_(i) for each pagew_(i). In general, a hub is a page with many outgoing links and anauthority is a page with many incoming links. The hub and authorityscores reflect how heavily a page serves as a reference or is referenceditself. The values for the hub and authority scores are determined asfollows, respectively:

$\begin{matrix}{h_{i} = {\sum\limits_{j}\; C_{i,j}}} & (3) \\{a_{i} = {\sum\limits_{j}\; C_{j,i}}} & (4)\end{matrix}$

The next step in determining the propagation matrix is to determinetransition counts and page views, block 44. In one embodiment, eachtransition count, T_(i,j), represents actual user behavior on theInternet in terms of how many times a user views a page w_(i) and thendirectly views another page w_(j) (without viewing any interveningpages). All the transition counts are represented in matrix form whereinT_(i,j) represents each individual transition count. Pageviews representthe number of times a page was viewed and is related to the transitioncounts.

$\begin{matrix}{v_{i} = {\sum\limits_{j}\; T_{i,j}}} & (5)\end{matrix}$

Then the hyperlink connectivity matrix, hub score, authority score,transition counts, and pageviews are all used to create the propagationmatrix, block 46. The propagation matrix P is created using thefollowing formula:

$\begin{matrix}{P_{i,j} = \frac{{f\left( C_{i,j} \right)} + {g\left( {C_{i,j,}a_{i}} \right)} + {h\left( {T_{i,j},v_{i}} \right)}}{{F\left( h_{i} \right)} + {G\left( a_{i} \right)} + {H\left( v_{i} \right)}}} & (6)\end{matrix}$

The functions F(h_(i)), G(a_(i)) and H(v_(i)) provide weights to the hubscores, authority scores and pageviews. These functions, F(h_(i)),G(a_(i)) and H(v_(i)), are monotonically increasing scalar functions ofnon-negative integers, h_(i), a_(i) and v_(i), respectively. Each ofthese functions corresponds to a weighing function, such as a stepfunction. For example:F(0)=0;  (7)F(h _(i))=F′ if 1≦Σh _(i) ≦x; and   (8)F(h _(i))=F″ if Σh _(i) >x,   (9)wherein F′>F″. This gives a lower significance to a hub score if it isbelow a threshold value “x” which indicates that insufficient data wasaccumulated. G(a_(i)) and H(v_(i)) are determined in a similar manner.However, the threshold value for G(a_(i)) will be a value “y” of a_(i)and the threshold value for H(v_(i)) will be a value “z” of v_(i).

The functions f(C_(i,j),h_(i)), g(C_(i,j),a_(i)) and h(T_(i,j),v_(i))represent the contributions of the links and transitions. Each functionis a weighted quotient of its arguments, except when its denominator iszero. For example, f(C_(i,j)):

$\begin{matrix}{{{f\left( C_{i,j} \right)} = {{{F\left( h_{i} \right)}\frac{C_{i,j}}{h_{i}}{if}\mspace{14mu} h_{i}} > 0}};{and}} & (10)\end{matrix}$f(C _(i,j),0)=0  (11)

The functions g(C_(i,j),a_(i)) and h(T_(i,j),v_(i)) are determined in asimilar manner.

As shown in FIG. 1, the next step in determining whether each page iscommercial is determining a commercial score for each page 20. Thisdetermination involves not only the propagation matrix, P, and thetransaction rating τ(w_(i)), but the spam score, σ(w_(i)), and qualityscore, q(w_(i)), as well. The transaction rating τ(w_(i)) and the spamscore σ(w_(i)) determine the weight of the different components. Thecommercial score is determined recursively for each page, w_(i), by thefollowing:

$\begin{matrix}{{\kappa^{\prime}(0)} = {\frac{{{A\tau}\left( w_{i} \right)} + {B_{q}\left( w_{i} \right)} + {\sigma\left( w_{i} \right)}}{A + B + 1}\mspace{11mu}{for}\mspace{14mu}{each}\mspace{14mu}{page}\mspace{14mu} w_{i}}} & (12)\end{matrix}$κ′(t)=ηP ^(T)κ′(t−1)+(1−η)κ′(0)  (13)κ=κ′(t′)  (14)

Where κ′(0) is the weighted average of the transaction rating, τ(w_(i)),the spam score, σ(w_(i)) and the quality score, q(w_(i)). A and B areweighing factors that determine the weight given to τ(w_(i)) andq(w_(i)), respectively. A and B may be selected by the search engineprovider or creator. The vector κ′(t) has an element κ′_(i)(t) for everypage examined w_(i). η is the propagation matrix weight and may also beset by the search engine provider or creator. η determines the degree towhich the propagation matrix effects the commercial score in the initialiterations. The symbol “t” indicates an incrementing integer that startsat one and increases by one for each iteration. Each iteration has thepotential to affect all w_(i). The iterations continue for apredetermined number of iterations “t′” or until there is littlevariation in the value of the commercial score:∥κ′(t′)−κ′(t′−1)∥ρ≦Δ  (15)

ρ is the norm-level and Δ is a commercial score variation value. Oncethe difference in values obtained from two subsequent iterations equalsor is less than the commercial score variation value, the iterationsstop and the commercial score is obtained 22.

All pages with a commercial score above or equal to a commercial scorethreshold value are filtered out and comprise the Spam-InclusiveCommercial Pages 22. Although they may often be considered a subset ofthe Commercial Pages, the Spam Pages are filtered out from theSpam-Inclusive Pages 24 to yield the Commercial Pages, because SpamPages are generally undesirable. The Commercial Pages are then placedinto the commercial category 26. Once the Commercial Pages and the SpamPages are filtered from the pages, the remaining pages are placed in thenon-commercial category. The non-commercial category may also includethe PCC Pages.

In another embodiment, pages are categorized into Commercial andNon-Commercial categories as described above, however Spam Pages are notseparated into a distinct category. Instead, the Spam Pages arecategorized as either Commercial or Non-commercial Pages depending onthe underlying commercial score assigned to that page and the thresholdscores for each category specified. Because Spam Pages may, in theory,be either commercial or non-commercial and because the inclusion of SpamPages may be useful for some users and/or in some applications, thisembodiment does not include a step for the identification and filter outSpam pages. By removing the identification and filtering of Spam Pages,this embodiment is more modularly compatible with existing searchengines because many existing search engines are equipped with their ownsystems for identifying and eliminating Spam Pages. In yet anotherembodiment, the Spam Pages are not removed from the commercial categorybecause Spam Pages do have potential value, for instance, as sales leadsfor a pay for performance search engine.

In another embodiment, categorization of Resulting Pages may becustomized by or for the user (including consumers, Site Providers andAdvertisers). In the first stage of the process, the user defines theircategorization preferences by entering such preferences through thesystem's user interface and then refining their selections until thedesired categorization is achieved. Both the categories themselves andhow the Resulting Pages are categorized can be customized. The systemcan be customized to categorize Resulting Pages into categoriesspecified by the user, using the previously described methods. Intowhich category a given Resulting Page is categorized can be effected byselecting any of the following alone or in combination: how PCC Pagesare categorized, the threshold levels, the ρ-norm level, parameters Aand B in equation (12), the number of iterations t′ for computing thecommercial score, commercial score variation value Δ, the criteria usedto determine which Resulting Pages are Commercial or PCC Pages and howmuch weight to give each criteria, the criteria used to determine thetransaction score, and the transaction score formula used to determinethe transaction rating (the “Categorization Criteria”).

The Categorization Criteria can all be chosen so that Resulting Pagesare categorized and presented in a variety of ways in order to satisfythe user's preferences. In general, the Categorization Criteria may bechosen empirically by manual-seeding the system with pre-selected pagesand examining the categories in which the pre-selected pages arecategorized and then adjusting the Categorization Criteria to tune thesystem until the desired categorizations are achieved. For example, asshown in FIG. 5A, the user hand-seeds the system 200 with pre-selectedpages for which the user knows the categories into which the pagesshould be placed 210. The user than inputs the user's preferences interms of the categories into which the pages are to be categorized andthe format in which the categorized results should be displayed 212. Theuser then sets the Categorization Criteria 214. The system thencategorizes and presents the categorized results to the user 216. Theuser then determines whether the system has categorized the pre-selectedpages into the desired categories 218. If the pre-selected pages are notcategorized in the desired categories, any one or combination of theCategorization Criteria may be altered and set in the system 214. Steps214, 216 and 218 may be repeated until the desired categorization isachieved.

In step 212, the user may set preferences for the way in which thecategorized results are displayed. The results obtained fromcategorizing the Resulting Pages may be displayed in a variety of ways.For instance, the user may specify that only Resulting Pages matching akeyword search are to be categorized and presented or that a specifictype or category of pages are to always be excluded, e.g. pornography ordebt relief advertisements. Additionally or alternatively, the user mayview the categorized pages contained in certain categories in a varietyof ways, including displaying by category or only displaying particularcategories while not others. Additionally or alternatively, the user mayspecify the order in which the categorized pages are to be displayed.For instance, the categorized pages may be displayed by category with apreferred category appearing first. Additionally or alternately,intermediate values such as the transaction score, transaction rating,hyperlink connectivity matrix, propagation matrix, transaction authorityand hub scores, the commercial, spam and quality scores may also bedisplayed. Additionally or alternately, the user may also request thatthe anchor text of the links be examined. If the anchor text containsthe keywords, the pages containing any number of the keywords would begiven a higher weighting than the links that do not contain any of thekeywords. Alternatively, links containing a greater number of keywordscan be given a higher weighting than those with a lower number.Customizing the display of categorized pages be accomplished using knowndisplay and presentation techniques.

Once the user has specified the categories, Categorization Criteria anddisplay preferences, a search 250 may be performed. As shown in FIG. 6,a search 250 begins when a user enters a search term or phrase into thesystem using a user interface 260. The system will then generate theResulting Pages according to any of a variety of known relevancemethods, including returning Resulting Pages that contain a keyword orthe keywords that match the search term or phrase (the search results)262. The system will then categorize the search results into categoriesspecified by the user so that the Categorization Criteria specified bythe user are satisfied 264. The system then presents the categorizedpages according to the user's presentation preferences 266.

In a further embodiment, the Commercial Pages may be used to generatesales leads. Using the URLs of the Commercial Pages, contact informationfor the companies hosting the Commercial Pages can be obtained from adomain name registry. The list of companies and their contactinformation can then be compiled to develop a list of sales leads. Asdepicted in FIG. 7 a system 270 for categorizing the Resulting Pagesgenerally includes the following steps: (a) assume that each ResultingPage is non-commercial in nature 272; (b) identify and filter out thepages that are commercial in nature into a first category 274; (c)identify and filter out existing advertiser client pages from the pagesin the first category 276; (d) gather contact information for theremaining pages (“lead pages”) 278; and (e) provide the lead pages andtheir associated contact information as sales leads 280 to, forinstance, a pay for performance search engine provider or any otherinterested party.

In another embodiment, advertisers are offered the opportunity to pay tohave their listings included in or excluded from, certain categories,using the techniques described in U.S. Pat. No. 6,269,361, incorporatedby reference, herein. The fee paid by the advertisers may be a functionof the prominence given their listing in a select category. In a furtherembodiment, only pages for which a fee has been paid will appear in thecommercial (or other designated) category. In one embodiment, acustomizable system for categorizing and presenting the records or theresults of a search of the records in a distributed database may beconfigured as an account management server or search engine serverassociated with a database search apparatus, such as the type disclosedin U.S. Pat. No. 6,269,361. The functions described herein andillustrated in FIGS. 1–8 may be implemented in any suitable manner.

One implementation is computer-readable source or object code thatcontrols a processor of a server or other computing device to performthe described functions. The computer-readable code may be implementedas an article including a computer-readable signal-bearing medium. Inone embodiment, the medium is a recordable data storage medium such as afloppy disk or a hard disk drive of a computer or a nonvolatile type ofsemiconductor memory. In another embodiment, the medium is a modulatedcarrier signal such as data read over a network such as the internet.The medium includes means in the medium for determining whether a pageis transactional, means in the medium for deriving a propagation matrixfor the page, and means in the medium for defining a commercial score asa function of the propagation matrix for the page. The various means maybe implemented as computer source code, computer-readable object code orany other suitable apparatus for controlling a processing device toperform the described function.

Another embodiment of the present invention constitutes an apparatus forcategorizing and presenting the records or the results of a search ofthe records in a distributed database over a distributed client-serverarchitecture is shown in FIG. 8. This search engine and database 100shown in FIG. 8 generally comprises a computing apparatus 110, 114, 118,120, memory devices 112 and 116, a server 124 and an interface 122. Thecomputing apparatuses 110, 114, 118, 120 may include any processors thatcan perform computations. The crawler 110 is a computing apparatus thatis connected to the Internet via a network and goes to every page andmakes a copy of the page (the “Resulting Page”), including the staticand/or dynamic content, whether text, image, audio, video or stillimages and stores the copy in the Internet cache 112. Alternatively,only a discrete number of parts of each Resulting Page, such as the URLand/or title are copied and stored in the Internet cache 112. Then theindexer 114 assigns each Resulting Page copy, or portion thereof, anaddress in the Internet cache 112 by (the “Internet cache address”). Theindexer also generates search terms for each Resulting Page and storesthese search terms with the associated Internet cache address, in theInternet index 116. The Internet cache and the Internet index would useapproximately 30 terabytes and 5 terabytes, respectively, given thecurrent size of the Internet.

The transaction score generator 118 uses the information contained inthe copies of each Resulting Page (or portions thereof) stored in theInternet cache 112 to generate the transaction scores. These transactionscores are then stored in the Internet cache 112 with their associatedResulting Internet pages. The category assignor 120 uses the transactionscores and other information stored in the Internet cache 112 togenerate the propagation matrix and assign a category to each ResultingPage. The transaction scores, commercial scores, quality scores, spamscores and categories for each page are stored in the Internet cache 112with their associated pages. The customizable threshold values p, normparameter p, commercial score variation values Δ, etc. may be stored onthe client or server side of the system as is well known to thoseskilled in the art. A search server 124 is coupled to the Internet index116 and the Internet cache 112 and allows the apparatus to connect tothe users via the system's user interface 122. The system's userinterface 122 may be a browser or it may be agent or applicationsoftware.

A user desiring to search the Internet may use the system user interface122 to connect to the search server 124 via the Internet. If the systemuser interface 122 is a browser, it sends the user's search request tothe search server 124 via the internet. Alternatively, if the userinterface 122 is agent software, the agent sends an automated searchrequest over the internet. Additionally, the user interface 122 maycomprises both a browser and agent software and send an automated searchrequest to the search server 124 over the Internet. The search server124 then uses the Internet index 116 to determine which Resulting Pagesare associated with the user's search terms. These Resulting Pages arethen retrieved from the Internet Cache 112 and presented to the user viathe user interface 122 in the manner specified by the user.

From the foregoing, it can be seen that the presently disclosedembodiments provide a method and apparatus for categorizing andpresenting select elements of a distributed database. Further advantagesinclude providing advertisers, search service providers and users with asearch engine and database that permits the customizable categorizationof search results and providing a method and apparatus for filteringsearch results so that only a desired category or categories of searchresults are returned or displayed.

Further benefits of the presently disclosed embodiments includeproviding to users, advertisers, search site providers and search engineproviders a method of customizing searches to search and/or displaysearch results according to category or criteria, and providingadvertisers with a method for controlling with which other links thatadvertiser's products and/or services are categorized and displayed.Still further, the present embodiments disclose providing a method ofidentifying the nature of a site and providing a search engine capableof categorizing search results, as well as providing a search enginethat is customizable by users and advertisers.

Although the invention has been described in terms of specificembodiments and applications, persons skilled in the art can, in lightof this disclosure, generate additional embodiments without exceedingthe scope or departing from the spirit of the claimed invention. Forexample, the system and methods presented herein may be applied not justto databases accessed over the Internet, but to any distributeddatabase. Furthermore, there is a vast variety of categories into whichthe pages or documents may be placed and in the criteria used to placethem there. Accordingly, it is to be understood that the drawings anddescriptions in this disclosure are proffered to facilitatecomprehension of the invention and should not be construed to limit thescope thereof.

We claim:
 1. A computer-implemented method for categorizing ResultingPages into categories, comprising: designating a first category ascommercial pages and a second category as informational pages;determining a quality score q(wi) for each Resulting Page; determining atransactional rating τ(w_(i))for each Resulting Page, deriving apropagation matrix P; determining a commercial score κ for eachResulting Page; filtering out all Resulting Pages that meet or exceed acommercial score threshold value; wherein the Resulting Pages that meetor exceed the commercial page threshold value are placed in the firstcategory and all remaining Resulting Pages are placed in the secondcategory, wherein the determining the transactional rating τ(w_(i))comprises determining whether each Resulting Page meets select criteria,determining how strongly each Resulting Page meets the select criteria,determining a transactional score for each page, and determining thetransactional rating for each page from the transactional score, andwherein determining a transactional score for each page comprisescreating a vector for each Resulting Page αk(w_(i)), wherein each vectorcontains a plurality of elements αk_(n)(w_(i)), wherein each of theplurality of elements αk_(n)(w_(i)) is a Boolean value that reflects howstrongly each of the Resulting Pages meets each of the select criteria.2. A computer-implemented method for categorizing Resulting Pages intocategories, comprising: designating a first category as commercial pagesand a second category as informational pages; determining a qualityscore q(wi) for each Resulting Page; determining a transactional ratingτ(w_(i)) for each Resulting Page, deriving a propagation matrix P;determining a commercial score κ for each Resulting Page; filtering outall Resulting Pages that meet or exceed a commercial score thresholdvalue; wherein the Resulting Pages that meet or exceed the commercialpage threshold value are placed in the first category and all remainingResulting Pages are placed in the second category, wherein thedetermining the transactional rating τ(w_(i)) comprises determiningwhether each Resulting Page meets select criteria, determining howstrongly each Resulting Page meets the select criteria, determining atransactional score for each page, and determining the transactionalrating for each page from the transactional score, and whereindetermining a transactional score for each page comprises creating avector for each Resulting Page βk(w_(i)), wherein each vector contains aplurality of elements βk_(n)(w_(i)), wherein each of the plurality ofelements βk_(n)(w_(i)) is a weighted value that reflects how stronglyeach of the Resulting Pages meets each of the select criteria.
 3. Acomputer-implemented method for categorizing Resulting Pages intocategories, comprising: designating a first category as commercial pagesand a second category as informational pages; determining a qualityscore q(wi) for each Resulting Page; determining a transactional ratingτ(w_(i)) for each Resulting Page, deriving a propagation matrix P;determining a commercial score κ for each Resulting Page; filtering outall Resulting Pages that meet or exceed a commercial score thresholdvalue; wherein the Resulting Pages that meet or exceed the commercialpage threshold value are placed in the first category and all remainingResulting Pages are placed in the second category, wherein thedetermining the transactional rating τ(w_(i)) comprises determiningwhether each Resulting Page meets select criteria, determining howstrongly each Resulting Page meets the select criteria, determining atransactional score for each page, and determining the transactionalrating for each page from the transactional score, and whereindetermining the transactional rating τ(w_(i)) for each page from thetransactional score comprises evaluating a relationship between thetransactional rating τ(w_(i)), and a p-norm of a vector for eachResulting Page αk(w_(i)) wherein the relationship is defined by${\tau\left( w_{i} \right)} = {{{\alpha\left( w_{i} \right)}}_{\rho} = {\left( {\sum\limits_{i = 1}^{n}\;{{\alpha\left( w_{i} \right)}}^{\rho}} \right)^{- \rho}.}}$4. A computer-implemented method for categorizing Resulting Pages intocategories, as claimed in claim 3 wherein ρ=2.
 5. A computer-implementedmethod for categorizing Resulting Pages into categories, comprising:designating a first category as commercial pages and a second categoryas informational pages; determining a quality score q(wi) for eachResulting Page; determining a transactional rating τ(w_(i)) for eachResulting Page, deriving a propagation matrix P; determining acommercial score κ for each Resulting Page; filtering out all ResultingPages that meet or exceed a commercial score threshold value; whereinthe Resulting Pages that meet or exceed the commercial page thresholdvalue are placed in the first category and all remaining Resulting Pagesare placed in the second category, wherein the determining thetransactional rating τ(w_(i)) comprises determining whether eachResulting Page meets select criteria, determining how strongly eachResulting Page meets the select criteria, determining a transactionalscore for each page, and determining the transactional rating for eachpage from the transactional score, and wherein determining thetransactional rating τ(w_(i)) for each page from the transactional scorecomprises evaluating a relationship between the transactional ratingτ(w_(i)) and a p-norm of a vector for each Resulting Page βk(w_(i))wherein the relationship is defined by${\tau\left( w_{i} \right)} = {{{\beta\left( w_{i} \right)}}_{\rho} = {\left( {\sum\limits_{i = 1}^{n}\;{{\beta\left( w_{i} \right)}}^{\rho}} \right)^{- \rho}.}}$6. A computer-implemented method for categorizing Resulting Pages intocategories, as claimed in claim 5 wherein ρ=2.
 7. A computer-implementedmethod for categorizing Resulting Pages into categories, comprising:designating a first category as commercial pages and a second categoryas informational pages; determining a quality score q(wi) for eachResulting Page; determining a transactional rating for each ResultingPage τ(w_(i)); deriving a propagation matrix; P determining a commercialscore κ for each Resulting Page; filtering out all Resulting Pages thatmeet or exceed a commercial score threshold value; wherein the ResultingPages that meet or exceed the commercial page threshold value are placedin the first category and all remaining Resulting Pages are placed inthe second category wherein deriving a propagation matrix, comprises:creating a hyperlink connectivity matrix C containing elements Ci,j;calculating a plurality of authority scores ai and a plurality of hubscores hi; calculating a plurality of transition counts Ti,j and aplurality of pageviews vi for each Resulting Page; and creating thepropagation matrix P containing propagation matix elements Pi,j.
 8. Acomputer-implemented method for categorizing Resulting Pages intocategories, as claimed in claim 7, wherein creating a hyperlinkconnectivity matrix C comprises: representing a link structure of theInternet in a matrix.
 9. A computer-implemented method for categorizingResulting Pages into categories, as claimed in claim 8, wherein the linkstructure if the Internet is represented by examining a number of linksfrom each Resulting Page to each Resulting Page.
 10. Acomputer-implemented method for categorizing Resulting Pages intocategories, as claimed in claim 7, wherein the plurality of hub scoreshi and the plurality of authority scores are related to the hyperlinkconnectivity matrix C and wherein the plurality of authority scores aiare defined as: $a_{i} = {\sum\limits_{j}\; C_{j,i}}$ and wherein theplurality of hub scores are defined as:${h_{i} = {\sum\limits_{j}C_{i,j}}}\;,$ respectively.
 11. Acomputer-implemented method for categorizing Resulting Pages intocategories, as claimed in claim 7, wherein the plurality of pageviews viare related to the plurality of transition counts Ti,j and are definedby: $v_{i} = {\sum\limits_{j}{T_{i,j}\;.}}$
 12. A computer-implementedmethod for categorizing Resulting Pages into categories, as claimed inclaim 11, wherein the propagation matrix is a function of the hyperlinkconnectivity matrix, the plurality of hub scores, the plurality ofauthority scores, the plurality of transition counts and the pluralitypageviews.
 13. A computer-implemented method for categorizing ResultingPages into categories, as claimed in claim 11, wherein calculating thepropagation matrix further comprises weighting the plurality of hubscores, the plurality of authority scores, and the plurality pageviews.14. A computer-implemented method for categorizing Resulting Pages intocategories, as claimed in claim 11, wherein the propagation matrix P isa further function of weighing functions F(hi), G(ai) and H(vi), andwherein the propagation matrix P is defined as:$P_{i,j} = {\frac{{f\left( C_{i,j} \right)} + {g\left( {C_{i,j},a_{i}} \right)} + {h\left( {T_{i,j},v_{i}} \right)}}{{F\left( h_{i} \right)} + {G\left( a_{i} \right)} + {H\left( v_{i} \right)}}.}$15. A computer-implemented method for categorizing Resulting Pages intocategories, as claimed in claim 14, wherein each of the weightingfunctions comprises a step function.
 16. A computer-implemented methodfor categorizing Resulting Pages into categories, as claimed in claim15, wherein the commercial score κ for each Resulting Page wi isdetermined recursively.
 17. A computer-implemented method forcategorizing Resulting Pages into categories, as claimed in claim 16,wherein the commercial score κ is recursively determined over titerations from a transverse of the propagation matrix P^(T), apropagation matrix weight η, and a commercial score initial value κ′(0),wherein κ′(0) is weighted by select quantities A and B and defined as:${\kappa^{\prime}(0)} = \frac{{A\;{\tau\left( w_{i} \right)}} + {{Bq}\left( w_{i} \right)} + {\sigma\left( w_{i} \right)}}{A + B + 1}$and a prior iteration of the commercial score κ′(t), wherein κ′(t) isdefined as: κ′(t)=ηP^(T)κ′(t−1)+(1−η)κ′(0), and wherein κ=κ′(t′).
 18. Acomputer-implemented method for categorizing Resulting Pages intocategories, comprising: designating a first category as commercial pagesand a second category as informational pages; determining a qualityscore q(wi) for each Resulting Page; determining a transactional ratingfor each Resulting Page τ(w_(i)); deriving a propagation matrix; Pdetermining a commercial score κ for each Resulting Page; filtering outall Resulting Pages that meet or exceed a commercial score thresholdvalue; wherein the Resulting Pages that meet or exceed the commercialpage threshold value are placed in the first category and all remainingResulting Pages are placed in the second category, and designating athird category as spam pages; and determining a spam score σ(wi) foreach Resulting Page; wherein determining the commercial score κ for eachResulting Page is recursively determined over t iterations from atransverse of the propagation matrix P^(T), propagation matrix weight ηand commercial score initial value κ′(0), wherein κ′(0) is weighted byselect quantities A and B and defined as:${\kappa^{\prime}(0)} = \frac{{A\;{\tau\left( w_{i} \right)}} + {{Bq}\left( w_{i} \right)} + {\sigma\left( w_{i} \right)}}{A + B + 1}$and a prior iteration of the commercial score κ′(t), wherein κ′(t) isdefined as: κ′(t)=ηP^(T)κ′(t−1)+(1−η)κ′(0), and wherein κ=κ′(t′).